✅Quality Control

Verification flow

Quality controls are made at two levels:

• task level: making sure each task has been properly done

• job level: making sure all tasks have been completed consistently with requirements

Our quality check relies upon 3 levels of validation, both fully customizable based on clients' needs:

• Consensus based verification: verification is done by "community verifiers", and validation is made based on consensus, with "slashing" of incorrect productions or colluding verifiers to incentivize good behaviours

• AI-powered review: specifically trained LLMs help screen compliant tasks to increase verification throughput

• Curator validation: verification is done by specifically trained curators

Depending upon clients' needs and projects' complexity, it is possible to combine any of these 3 verification mechanisms to provide stronger quality checks

Job validation triggers the distribution of rewards, unless specified otherwise in instructions

QC core principles

Ta-da's platform is build so as to enable quality controls at scale, guaranteeing better data quality, at

• Redundancy models (e.g., consensus, majority voting) can be expensive and slow.

• Automated quality checks often struggle with edge cases or subtle errors.

• Data injection and real-time feedback loops

• Integration with ML pipelines or client APIs to close the loop between data needs and data supply.

• Analytics dashboards to help data consumers measure dataset evolution, annotation variance, and coverage gaps.